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2.
Sensors (Basel) ; 21(24)2021 Dec 20.
Article in English | MEDLINE | ID: covidwho-1580509

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548-49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000-3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141-2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895-2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.


Subject(s)
COVID-19 , Hospital Mortality , Humans , Machine Learning , Prognosis , Retrospective Studies , SARS-CoV-2 , Survival Analysis
3.
Rev Med Virol ; 31(6): e2221, 2021 11.
Article in English | MEDLINE | ID: covidwho-1575100

ABSTRACT

The current pandemic caused by SARS-CoV-2 virus infection is known as Covid-19 (coronavirus disease 2019). This disease can be asymptomatic or can affect multiple organ systems. Damage induced by the virus is related to dysfunctional activity of the immune system, but the activity of molecules such as C-reactive protein (CRP) as a factor capable of inducing an inflammatory status that may be involved in the severe evolution of the disease, has not been extensively evaluated. A systematic review was performed using the NCBI-PubMed database to find articles related to Covid-19 immunity, inflammatory response, and CRP published from December 2019 to December 2020. High levels of CRP were found in patients with severe evolution of Covid-19 in which several organ systems were affected and in patients who died. CRP activates complement, induces the production of pro-inflammatory cytokines and induces apoptosis which, together with the inflammatory status during the disease, can lead to a severe outcome. Several drugs can decrease the level or block the effect of CRP and might be useful in the treatment of Covid-19. From this review it is reasonable to conclude that CRP is a factor that can contribute to severe evolution of Covid-19 and that the use of drugs able to lower CRP levels or block its activity should be evaluated in randomized controlled clinical trials.


Subject(s)
Anti-Inflammatory Agents/therapeutic use , C-Reactive Protein/antagonists & inhibitors , COVID-19/drug therapy , Complement System Proteins/immunology , Cytokine Release Syndrome/drug therapy , SARS-CoV-2/pathogenicity , ADAM17 Protein/antagonists & inhibitors , ADAM17 Protein/genetics , ADAM17 Protein/immunology , Angiotensin-Converting Enzyme 2/antagonists & inhibitors , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/immunology , Biomarkers/blood , C-Reactive Protein/genetics , C-Reactive Protein/immunology , COVID-19/immunology , COVID-19/pathology , COVID-19/virology , Celecoxib/therapeutic use , Complement System Proteins/genetics , Cytokine Release Syndrome/immunology , Cytokine Release Syndrome/pathology , Cytokine Release Syndrome/virology , Cytokines/antagonists & inhibitors , Cytokines/genetics , Cytokines/immunology , Disease Progression , Doxycycline/therapeutic use , Gene Expression Regulation , Humans , Randomized Controlled Trials as Topic , Severity of Illness Index , Survival Analysis
4.
J Med Internet Res ; 23(2): e23458, 2021 02 26.
Article in English | MEDLINE | ID: covidwho-1574596

ABSTRACT

BACKGROUND: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. OBJECTIVE: In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. METHODS: Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. RESULTS: Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). CONCLUSIONS: We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning-based clinical decision support tools.


Subject(s)
COVID-19/mortality , Machine Learning , COVID-19/virology , Female , Humans , Male , Middle Aged , Models, Statistical , Pandemics , Retrospective Studies , SARS-CoV-2/isolation & purification , Survival Analysis
5.
Front Immunol ; 12: 729251, 2021.
Article in English | MEDLINE | ID: covidwho-1573871

ABSTRACT

Introduction: The World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic on March 11, 2020. Two vaccine types were developed using two different technologies: viral vectors and mRNA. Thrombosis is one of the most severe and atypical adverse effects of vaccines. This study aimed to analyze published cases of thrombosis after COVID-19 vaccinations to identify patients' features, potential pathophysiological mechanisms, timing of appearance of the adverse events, and other critical issues. Materials and Methods: We performed a systematic electronic search of scientific articles regarding COVID-19 vaccine-related thrombosis and its complications on the PubMed (MEDLINE) database and through manual searches. We selected 10 out of 50 articles from February 1 to May 5, 2021 and performed a descriptive analysis of the adverse events caused by the mRNA-based Pfizer and Moderna vaccines and the adenovirus-based AstraZeneca vaccine. Results: In the articles on the Pfizer and Moderna vaccines, the sample consisted of three male patients with age heterogeneity. The time from vaccination to admission was ≤3 days in all cases; all patients presented signs of petechiae/purpura at admission, with a low platelet count. In the studies on the AstraZeneca vaccine, the sample consisted of 58 individuals with a high age heterogeneity and a high female prevalence. Symptoms appeared around the ninth day, and headache was the most common symptom. The platelet count was below the lower limit of the normal range. All patients except one were positive for PF4 antibodies. The cerebral venous sinus was the most affected site. Death was the most prevalent outcome in all studies, except for one study in which most of the patients remained alive. Discussion: Vaccine-induced thrombotic thrombocytopenia (VITT) is an unknown nosological phenomenon secondary to inoculation with the COVID-19 vaccine. Several hypotheses have been formulated regarding its physiopathological mechanism. Recent studies have assumed a mechanism that is assimilable to heparin-induced thrombocytopenia, with protagonist antibodies against the PF4-polyanion complex. Viral DNA has a negative charge and can bind to PF4, causing VITT. New experimental studies have assumed that thrombosis is related to a soluble adenoviral protein spike variant, originating from splicing events, which cause important endothelial inflammatory events, and binding to endothelial cells expressing ACE2. Conclusion: Further studies are needed to better identify VITT's pathophysiological mechanisms and genetic, demographic, or clinical predisposition of high-risk patients, to investigate the correlation of VITT with the different vaccine types, and to test the significance of the findings.


Subject(s)
/immunology , COVID-19/immunology , SARS-CoV-2/physiology , Thrombosis/epidemiology , /adverse effects , Antigen-Antibody Complex/metabolism , COVID-19/complications , COVID-19/epidemiology , Cerebral Veins/metabolism , Cerebral Veins/pathology , Female , Headache , Humans , Mass Vaccination , Platelet Factor 4/immunology , Sex Factors , Survival Analysis , Thrombosis/etiology , Thrombosis/mortality
6.
Dis Markers ; 2021: 6304189, 2021.
Article in English | MEDLINE | ID: covidwho-1553755

ABSTRACT

Background: Early identification of patients with severe coronavirus disease (COVID-19) at an increased risk of progression may promote more individualized treatment schemes and optimize the use of medical resources. This study is aimed at investigating the utility of the C-reactive protein to albumin (CRP/Alb) ratio for early risk stratification of patients. Methods: We retrospectively reviewed 557 patients with COVID-19 with confirmed outcomes (discharged or deceased) admitted to the West Court of Union Hospital, Wuhan, China, between January 29, 2020 and April 8, 2020. Patients with severe COVID-19 (n = 465) were divided into stable (n = 409) and progressive (n = 56) groups according to whether they progressed to critical illness or death during hospitalization. To predict disease progression, the CRP/Alb ratio was evaluated on admission. Results: The levels of new biomarkers, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, CRP/Alb ratio, and systemic immune-inflammation index, were higher in patients with progressive disease than in those with stable disease. Correlation analysis showed that the CRP/Alb ratio had the strongest positive correlation with the sequential organ failure assessment score and length of hospital stay in survivors. Multivariate logistic regression analysis showed that percutaneous oxygen saturation (SpO2), D-dimer levels, and the CRP/Alb ratio were risk factors for disease progression. To predict clinical progression, the areas under the receiver operating characteristic curves of Alb, CRP, CRP/Alb ratio, SpO2, and D-dimer were 0.769, 0.838, 0.866, 0.107, and 0.748, respectively. Moreover, patients with a high CRP/Alb ratio (≥1.843) had a markedly higher rate of clinical deterioration (log - rank p < 0.001). A higher CRP/Alb ratio (≥1.843) was also closely associated with higher rates of hospital mortality, ICU admission, invasive mechanical ventilation, and a longer hospital stay. Conclusion: The CRP/Alb ratio can predict the risk of progression to critical disease or death early, providing a promising prognostic biomarker for risk stratification and clinical management of patients with severe COVID-19.


Subject(s)
C-Reactive Protein/metabolism , COVID-19/diagnosis , Coronary Disease/diagnosis , Hypertension/diagnosis , Pulmonary Disease, Chronic Obstructive/diagnosis , SARS-CoV-2/pathogenicity , Serum Albumin, Human/metabolism , Aged , Area Under Curve , Biomarkers/blood , Blood Platelets/pathology , Blood Platelets/virology , COVID-19/epidemiology , COVID-19/mortality , COVID-19/virology , China/epidemiology , Comorbidity , Coronary Disease/epidemiology , Coronary Disease/mortality , Coronary Disease/virology , Disease Progression , Early Diagnosis , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , Hypertension/epidemiology , Hypertension/mortality , Hypertension/virology , Length of Stay/statistics & numerical data , Lymphocytes/pathology , Lymphocytes/virology , Male , Middle Aged , Neutrophils/pathology , Neutrophils/virology , Prognosis , Pulmonary Disease, Chronic Obstructive/epidemiology , Pulmonary Disease, Chronic Obstructive/mortality , Pulmonary Disease, Chronic Obstructive/virology , ROC Curve , Retrospective Studies , SARS-CoV-2/growth & development , Severity of Illness Index , Survival Analysis
7.
Int J Obes (Lond) ; 45(12): 2617-2622, 2021 12.
Article in English | MEDLINE | ID: covidwho-1526061

ABSTRACT

BACKGROUND: The impact of obesity on outcomes in acute respiratory distress syndrome (ARDS) is not well understood and remains controversial. Recent studies suggest that obesity might be associated with higher morbidity and mortality in respiratory disease caused by SARS-CoV-2 (COVID-19 disease). Our objective was to evaluate the association between obesity and hospital mortality in critical COVID-19 patients. METHODS: We conducted a retrospective cohort study in a tertiary academic center located in Montréal between March and August 2020. We included all consecutive adult patients admitted to the ICU for COVID-19-confirmed respiratory disease. Our main outcome was hospital mortality. We estimated the association between obesity, using the body mass index as a continuous variable, and hospital survival by fitting a multivariable Cox proportional hazards model. RESULTS: We included 94 patients. Median [q1, q3] body mass index (BMI) was 29 [26-32] kg/m2 and 37% of patients were obese (defined as BMI > 30 kg/m2). Hospital mortality for the entire cohort was 33%. BMI was significantly associated with hospital mortality (hazard ratio [HR] = 2.49 per 10 units BMI; 95% CI, from 1.69 to 3.70; p < 0.001) even after adjustment for sex, age and obesity-related comorbidities (adjusted HR = 3.50; 95% CI from 2.03 to 6.02; p < 0.001). CONCLUSIONS: Obesity was prevalent in hospitalized patients with critical illness secondary to COVID-19 disease and a higher BMI was associated with higher hospital mortality. Further studies are needed to validate this association and to better understand its underlying mechanisms.


Subject(s)
COVID-19/mortality , Hospital Mortality , Obesity/epidemiology , Adult , Aged , Body Mass Index , Comorbidity , Critical Illness , Female , Humans , Male , Middle Aged , Quebec , Retrospective Studies , Survival Analysis
8.
Turk J Med Sci ; 51(4): 1665-1674, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1526879

ABSTRACT

Background/aim: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the predictors of mortality in critically ill patients admitted to the intensive care unit (ICU) are not yet well understood. In this study, we aimed to investigate the risk factors associated with ICU mortality in our hospital. Materials and methods: In this single-centered retrospective study, we enrolled 86 critically ill adult patients with COVID-19 admitted to ICU of Dokuz Eylül University Hospital (Izmir, Turkey) between 18 March 2020 and 31 October 2020. Data on demographic information, preexisting comorbidities, treatments, the laboratory findings at ICU admission, and clinical outcomes were collected. The chest computerized tomography (CT) of the patients were evaluated specifically for COVID-19 and CT score was calculated. Data of the survivors and nonsurvivors were compared with survival analysis to identify risk factors of mortality in the ICU. Results: The mean age of the patients was 71.1 ± 14.1 years. The patients were predominantly male. The most common comorbidity in patients was hypertension. ICU mortality was 62.8%. Being over 60 years old, CT score > 15, acute physiology and chronic health evaluation (APACHE) II score ≥ 15, having dementia, treatment without favipiravir, base excess in blood gas analysis ≤ ­2.0, WBC > 10,000/mm3, D-dimer > 1.6 µg/mL, troponin > 24 ng/L, Na ≥ 145 mmol/L were considered to link with ICU mortality according to Kaplan­Meier curves (log-rank test, p < 0.05). The APACHE II score (HR: 1.055, 95% CI: 1.021­1.090) and chest CT score (HR: 2.411, 95% CI:1.193­4.875) were associated with ICU mortality in the cox proportional-hazard regression model adjusted for age, dementia, favipiravir treatment and troponin. Howewer, no difference was found between survivors and nonsurvivors in terms of intubation timing. Conclusions: COVID-19 patients have a high ICU admission and mortality rate. Studies in the ICU are also crucial in this respect. In our study, we investigated the ICU mortality risk factors of COVID-19 patients. We determined a predictive mortality model consisting of APACHE II score and chest CT score. It was thought that this feasible and practical model would assist in making clinical decisions.


Subject(s)
COVID-19/diagnostic imaging , COVID-19/mortality , Critical Care/methods , Hospital Mortality , Intubation, Intratracheal/methods , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Intensive Care Units , Intubation, Intratracheal/statistics & numerical data , Lung/diagnostic imaging , Male , Middle Aged , Retrospective Studies , Risk Factors , SARS-CoV-2 , Survival Analysis , Time Factors , Turkey/epidemiology , Young Adult
9.
Int J Lab Hematol ; 43 Suppl 1: 137-141, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1526369

ABSTRACT

INTRODUCTION: Eosinopenia has been observed during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. This study evaluated the role of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection. METHODS: Information on 429 patients with confirmed COVID-19, admitted to Apollo Hospitals, Chennai, India between 04 June 2020 to 15 August 2020, was retrospectively collected through electronic records and analysed. RESULTS: 79.25% of the patients included in the study had eosinopenia on admission. The median eosinophil count in COVID-19-positive patients was 0.015 × 109 /L, and in negative patients, it was 0.249 × 109 /L. Eighteen per cent of the positive patients presented with 0 eosinophil count. Eosinopenia for early diagnosis of COVID-19 had a sensitivity of 80.68% and specificity of 100% with an accuracy of 85.24. Role of eosinopenia in prognostication of COVID-19 was found to be insignificant. There was no statistically significant difference between the median eosinophil counts in survivors and nonsurvivors. Eosinophil trends during the course of disease were found to be similar between survivors and nonsurvivors. CONCLUSIONS: Eosinopenia on admission is a reliable and convenient early diagnostic marker for COVID-19 infection, helping in early identification, triaging and isolation of the patients till nucleic acid test results are available. Role of eosinopenia as a prognostic indicator is insignificant.


Subject(s)
COVID-19 Testing/methods , COVID-19/blood , Eosinophils , Leukocyte Count , Leukopenia/etiology , Area Under Curve , Biomarkers , COVID-19/diagnosis , COVID-19/mortality , Eosinophilia/blood , Eosinophilia/etiology , Humans , India , Leukopenia/blood , Prognosis , ROC Curve , Retrospective Studies , Selection Bias , Sensitivity and Specificity , Survival Analysis
10.
Lab Med ; 52(5): 493-498, 2021 Sep 01.
Article in English | MEDLINE | ID: covidwho-1526169

ABSTRACT

OBJECTIVE: The aim of the study was to assess the role of midregional proadrenomedullin (MR-proADM) in patients with COVID-19. METHODS: We included 110 patients hospitalized for COVID-19. Biochemical biomarkers, including MR-proADM, were measured at admission. The association of plasma MR-proADM levels with COVID-19 severity, defined as a requirement for mechanical ventilation or in-hospital mortality, was evaluated. RESULTS: Patients showed increased levels of MR-proADM. In addition, MR-proADM was higher in patients who died during hospitalization than in patients who survived (median, 2.59 nmol/L; interquartile range, 2.3-2.95 vs median, 0.82 nmol/L; interquartile range, 0.57-1.03; P <.0001). Receiver operating characteristic curve analysis showed good accuracy of MR-proADM for predicting mortality. A MR-proADM value of 1.73 nmol/L was established as the best cutoff value, with 90% sensitivity and 95% specificity (P <.0001). CONCLUSION: We found that MR-proADM could represent a prognostic biomarker of COVID-19.


Subject(s)
Adrenomedullin/blood , COVID-19/diagnosis , Hypertension/diagnosis , Lung Diseases/diagnosis , Protein Precursors/blood , Aged , Alanine Transaminase/blood , Aspartate Aminotransferases/blood , Biomarkers/blood , C-Reactive Protein/metabolism , COVID-19/blood , COVID-19/mortality , COVID-19/virology , Comorbidity , Female , Humans , Hypertension/blood , Hypertension/mortality , Hypertension/virology , Interleukin-6/blood , Lung Diseases/blood , Lung Diseases/mortality , Lung Diseases/virology , Male , Middle Aged , Patient Selection , Prognosis , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Survival Analysis , Triage/methods
11.
Eur Rev Med Pharmacol Sci ; 25(21): 6731-6740, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1524861

ABSTRACT

OBJECTIVE: The aim of the study was to determine the association between platelet indices and disease severity, and outcomes of the patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a secondary hospital. PATIENTS AND METHODS: 722 hospitalized patients who had positive rRT-PCR for SARS-CoV-2 and/or typical findings of COVID-19 at chest computed tomography (CT) were enrolled in this study. Initial platelet count (PLT) and indices, including mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), MPV/PCT, MPV/PLT, PDW/PLT, PDW/PCT on admission and the third day of hospitalization, and their relationship with disease severity and outcomes were evaluated retrospectively. RESULTS: The mean age of the patients was 57.2±15.6 years (range: 16-94) and male/female ratio was 1.22. 81.9% of the patients had moderate and 11.8% had severe disease. 1.8% of the patients had thrombocytopenia at admission. The patients transferred to the intensive care unit (ICU) had significantly lower baseline lymphocyte counts, PLT, PCT, and 3rd day lymphocyte counts when compared with the patients in wards. ICU patients also had higher baseline CRP, LDH, ferritin, MPV/PCT, MPV/PLT, PDW/PLT, PDW/PCT ratios, and 3rd day PDW, CRP, LDH, and ferritin levels than the patients in wards. Mortality was associated with lower baseline lymphocyte counts, PLT, PCT, 3rd day lymphocyte counts and PCT. Higher baseline CRP, LDH, ferritin, MPV/PCT, PDW/PLT, PDW/PCT and 3rd day CRP, LDH, ferritin, procalcitonin, PDW, MPV/PCT, PDW/PLT, and PDW/PCT ratios were also associated with poor prognosis. CONCLUSIONS: Platelet count and ratios were significantly associated with mortality in patients with COVID-19.


Subject(s)
Blood Platelets/cytology , COVID-19/pathology , Adolescent , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19/mortality , COVID-19/virology , Female , Humans , Intensive Care Units , Male , Middle Aged , Platelet Count , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2/isolation & purification , Severity of Illness Index , Survival Analysis , Young Adult
12.
BMC Pulm Med ; 21(1): 354, 2021 Nov 08.
Article in English | MEDLINE | ID: covidwho-1505545

ABSTRACT

BACKGROUND: Intravenous immunoglobulin (IVIG) has been used as an immunomodulatory therapy to counteract severe systemic inflammation in coronavirus disease 2019 (COVID-19). But its use in COVID-19 related acute respiratory distress syndrome (ARDS) is not well established. METHODS: We conducted a retrospective analysis of electronic health records of COVID-19 patients admitted to intensive care units (ICUs) at Hazm Mebaireek General Hospital, Qatar, between March 7, 2020 and September 9, 2020. Patients receiving invasive mechanical ventilation for moderate-to-severe ARDS were divided into two groups based on whether they received IVIG therapy or not. The primary outcome was all-cause ICU mortality. Secondary outcomes studied were ventilator-free days and ICU-free days at day-28, and incidence of acute kidney injury (AKI). Propensity score matching was used to adjust for confounders, and the primary outcome was compared using competing-risks survival analysis. RESULTS: Among 590 patients included in the study, 400 received routine care, and 190 received IVIG therapy in addition to routine care. One hundred eighteen pairs were created after propensity score matching with no statistically significant differences between the groups. Overall ICU mortality in the study population was 27.1%, and in the matched cohort, it was 25.8%. Mortality was higher among IVIG-treated patients (36.4% vs. 15.3%; sHR 3.5; 95% CI 1.98-6.19; P < 0.001). Ventilator-free days and ICU-free days at day-28 were lower (P < 0.001 for both), and incidence of AKI was significantly higher (85.6% vs. 67.8%; P = 0.001) in the IVIG group. CONCLUSION: IVIG therapy in mechanically ventilated patients with COVID-19 related moderate-to-severe ARDS was associated with higher ICU mortality. A randomized clinical trial is needed to confirm this observation further.


Subject(s)
COVID-19/drug therapy , Immunoglobulins, Intravenous/therapeutic use , Immunologic Factors/therapeutic use , Respiratory Distress Syndrome/drug therapy , Administration, Intravenous , Adult , Aged , COVID-19/complications , COVID-19/mortality , Female , Humans , Male , Middle Aged , Propensity Score , Respiratory Distress Syndrome/mortality , Respiratory Distress Syndrome/virology , Retrospective Studies , Severity of Illness Index , Survival Analysis , Treatment Outcome
13.
BMC Public Health ; 21(1): 1985, 2021 11 02.
Article in English | MEDLINE | ID: covidwho-1501997

ABSTRACT

BACKGROUND: During March of 2020 the Centers for Disease Control and Prevention (CDC) announced non-pharmaceutical intervention (NPI) guidance as the primary mitigation strategy against growing COVID-19 community spread due to the absence of a vaccine or effective treatment at that time. CDC guidance states that NPIs are most effective when instituted in an early, targeted, and layered fashion. NPIs are effective in slowing spread, and measures should be custom-tailored to each population. This study examines factors associated with implementation and timing of NPI interventions across large public and private U.S. universities at the onset of the COVID-19 pandemic. METHODS: NPI decisions of interest include when U.S. universities canceled international travel, shifted to online learning, moved faculty/staff to remote work, limited campus housing, and closed campus for all non-essential personnel. Cox proportional hazard analyses of retrospective data were conducted to assess the time to NPI events. Hazard ratios were calculated for university governance, campus setting, religious affiliation, health infrastructure, faculty diversity, and student demographics. The methods control for variance inflation factors, COVID case prevalence, and time varying covariates of spring break and states' state of emergency (SOE) orders. This study captures NPI decisions at 575 U.S. universities during spring of 2020 which affected the movement of seven million students and two million employees. RESULTS: Universities located in districts represented by Democratic party congressional members reported earlier NPI implementation than Republican (Cox proportional hazard ratio (HR) range 0.61-0.80). University religious affiliation was not associated with the timing any of the NPI decisions. Universities with more diverse faculty showed an association with earlier NPI implementation (HR range 0.65-0.76). The existence of university-affiliated health infrastructure was not associated with NPI timing. CONCLUSION: University NPI implementation was largely driven by local COVID-19 epidemiology, culture and political concerns. The timing of university NPI decisions varied by regional politics, faculty demographics, university governance, campus setting, and foreign student prevalence adjusting for COVID-19 state case prevalence and spring break timing. Religious affiliation and presence of university health infrastructure were not associated with timing.


Subject(s)
COVID-19 , Pandemics , Humans , Retrospective Studies , SARS-CoV-2 , Survival Analysis , Universities
14.
Ethiop J Health Sci ; 31(4): 699-708, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1497599

ABSTRACT

Background: With the rising number of new cases of COVID-19, understanding the oxygen requirement of severe patients assists in identifying at risk groups and in making an informed decision on building hospitals capacity in terms of oxygen facility arrangement. Therefore, the study aimed to estimate time to getting off supplemental oxygen therapy and identify predictors among COVID-19 patients admitted to Millennium COVID-19 Care Center in Ethiopia. Methods: A prospective observational study was conducted among 244 consecutively admitted COVID-19 patients from July to September, 2020. Kaplan Meier plots, median survival times and Log-rank test were used to describe the data and compare survival distribution between groups. Cox proportional hazard survival model was used to identify determinants of time to getting off supplemental oxygen therapy, where hazard ratio (HR), P-value and 95%CI for HR were used for testing significance and interpretation of results. Results: Median time to getting off supplemental oxygen therapy among the studied population was 6 days (IQR,4.3-20.0). Factors that affect time to getting off supplemental oxygen therapy were age group (AHR=0.52,95%CI=0.32,0.84, p-value=0.008 for ≥70 years) and shortness of breath (AHR=0.71,95%CI=0.52,0.96, p-value=0.026). Conclusion: Average duration of supplemental oxygen therapy requirement among COVID-19 patients was 6 days and being 70 years and older and having shortness of breath were found to be associated with prolonged duration of supplemental oxygen therapy requirement. This result can be used as a guide in planning institutional resource allocation and patient management to provide a well-equipped care to prevent complications and death from the disease.


Subject(s)
COVID-19 , Aged , Ethiopia , Humans , Oxygen , Retrospective Studies , SARS-CoV-2 , Survival Analysis
15.
Front Immunol ; 12: 726283, 2021.
Article in English | MEDLINE | ID: covidwho-1497074

ABSTRACT

Severe status of coronavirus disease 2019 (COVID-19) is extremely associated to cytokine release. Moreover, it has been suggested that blood group is also associated with the prevalence and severity of this disease. However, the relationship between the cytokine profile and blood group remains unclear in COVID-19 patients. In this sense, we prospectively recruited 108 COVID-19 patients between March and April 2020 and divided according to ABO blood group. For the analysis of 45 cytokines, plasma samples were collected in the time of admission to hospital ward or intensive care unit and at the sixth day after hospital admission. The results show that there was a risk of more than two times lower of mechanical ventilation or death in patients with blood group O (log rank: p = 0.042). At first time, all statistically significant cytokine levels, except from hepatocyte growth factor, were higher in O blood group patients meanwhile the second time showed a significant drop, between 20% and 40%. In contrast, A/B/AB group presented a maintenance of cytokine levels during time. Hepatocyte growth factor showed a significant association with intubation or mortality risk in non-O blood group patients (OR: 4.229, 95% CI (2.064-8.665), p < 0.001) and also was the only one bad prognosis biomarker in O blood group patients (OR: 8.852, 95% CI (1.540-50.878), p = 0.015). Therefore, higher cytokine levels in O blood group are associated with a better outcome than A/B/AB group in COVID-19 patients.


Subject(s)
COVID-19/immunology , Cytokines/blood , SARS-CoV-2/physiology , ABO Blood-Group System , Aged , Biomarkers , COVID-19/diagnosis , COVID-19/mortality , Disease Progression , Female , Hepatocyte Growth Factor/blood , Hospitalization , Humans , Male , Middle Aged , Prognosis , Prospective Studies , Respiration, Artificial , Severity of Illness Index , Survival Analysis
16.
J Prim Care Community Health ; 12: 21501327211054281, 2021.
Article in English | MEDLINE | ID: covidwho-1496102

ABSTRACT

BACKGROUND: Length of hospital stay (LOS) for a disease is a vital estimate for healthcare logistics planning. The study aimed to illustrate the effect of factors elicited on arrival on LOS of the COVID-19 patients. MATERIALS AND METHODS: It was a retrospective, record based, unmatched, case control study using hospital records of 334 COVID-19 patients admitted in an East Indian tertiary healthcare facility during May to October 2020. Discharge from the hospital (cases/survivors) was considered as an event while death (control/non-survivors) as right censoring in the case-control survival analysis using cox proportional hazard model. RESULTS: Overall, we found the median LOS for the survivors to be 8 days [interquartile range (IQR): 7-10 days] while the same for the non-survivors was 6 days [IQR: 2-11 days]. In the multivariable cox-proportional hazard model; travel distance (>16 km) [adjusted hazard ratio (aHR): 0.69, 95% CI: (0.50-0.95)], mode of transport to the hospital (ambulance) [aHR: 0.62, 95% CI: (0.45-0.85)], breathlessness (yes) [aHR: 0.56, 95% CI: (0.40-0.77)], number of co-morbidities (1-2) [aHR: 0.66, 95% CI: (0.47-0.93)] (≥3) [aHR: 0.16, 95% CI: (0.04-0.65)], COPD/asthma (yes) [ [aHR: 0.11, 95% CI: (0.01-0.79)], DBP (<60/≥90) [aHR: 0.55, 95% CI: (0.35-0.86)] and qSOFA score (≥2) [aHR: 0.33, 95% CI: (0.12-0.92)] were the significant attributes affecting LOS of the COVID-19 patients. CONCLUSION: Factors elicited on arrival were found to be significantly associated with LOS. A scoring system inculcating these factors may be developed to predict LOS of the COVID-19 patients.


Subject(s)
COVID-19 , Case-Control Studies , Humans , India , Length of Stay , Retrospective Studies , Risk Factors , SARS-CoV-2 , Survival Analysis , Tertiary Healthcare
17.
J Acquir Immune Defic Syndr ; 88(4): 406-413, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1494140

ABSTRACT

BACKGROUND: There is a need to characterize patients with HIV with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SETTING: Multicenter registry of patients from 116 emergency departments in 27 US states. METHODS: Planned secondary analysis of patients with suspected SARS-CoV-2, with (n = 415) and without (n = 25,306) HIV. Descriptive statistics were used to compare patient information and clinical characteristics by SARS-CoV-2 and HIV status. Unadjusted and multivariable models were used to explore factors associated with death, intubation, and hospital length of stay. Kaplan-Meier curves were used to estimate survival by SARS-CoV-2 and HIV infection status. RESULTS: Patients with both SARS-CoV-2 and HIV and patients with SARS-CoV-2 but without HIV had similar admission rates (62.7% versus 58.6%, P = 0.24), hospitalization characteristics [eg, rates of admission to the intensive care unit from the emergency department (5.0% versus 6.3%, P = 0.45) and intubation (10% versus 13.3%, P = 0.17)], and rates of death (13.9% versus 15.1%, P = 0.65). They also had a similar cumulative risk of death (log-rank P = 0.72). However, patients with both HIV and SARS-CoV-2 infections compared with patients with HIV but without SAR-CoV-2 had worsened outcomes, including increased mortality (13.9% versus 5.1%, P < 0.01, log-rank P < 0.0001) and their deaths occurred sooner (median 11.5 versus 34 days, P < 0.01). CONCLUSIONS: Among emergency department patients with HIV, clinical outcomes associated with SARS-CoV-2 infection are not worse when compared with patients without HIV, but SARS-CoV-2 infection increased the risk of death in patients with HIV.


Subject(s)
COVID-19/complications , Emergency Service, Hospital/statistics & numerical data , HIV Infections/complications , COVID-19/therapy , COVID-19/virology , Female , Humans , Length of Stay , Male , Middle Aged , Risk Factors , SARS-CoV-2/isolation & purification , Survival Analysis , Treatment Outcome , United States
18.
J Infect Public Health ; 14(10): 1328-1333, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1492297

ABSTRACT

BACKGROUND: COVID-19 Coronavirus variants are emerging across the globe causing ongoing pandemics. It is important to estimate the case fatality ratio (CFR) during such an epidemic of a potentially fatal disease. METHODS: Firstly, we have performed a non-parametric approach for odds ratios with corresponding confidence intervals (CIs) and illustrated relative risks and cumulative mortality rates of COVID-19 data of Spain. We have demonstrated the modified non-parametric approach based on Kaplan-Meier (KM) technique using COVID-19 data of Italy. We have also performed the significance of characteristics of patients regarding outcome by age for both genders. Furthermore, we have applied a non-parametric cure model using Nadaraya-Watson weight to estimate cure-rate using Israel data. Simulations are based on R-software. RESULTS: The analytical illustrations of these approaches predict the effects of patients based on covariates in different scenarios. Sex differences are increased from ages less than 60 years to 60-69 years but decreased thereafter with the smallest sex difference at ages 80 years in a case for estimating both purposes RR (relative risk) and OR (odds ratio). The non-parametric approach investigates the range of cure-rate ranges from 5.3% to 9% and from 4% to 7% approximately for male and female respectively. The modified KM estimator performs for such censored data and detects the changes in CFR more rapidly for both genders and age-wise. CONCLUSION: Older-age, male-sex, number of comorbidities and access to timely health care are identified as some of the risk factors associated with COVID-19 mortality in Spain. The non-parametric approach has investigated the influence of covariates on models and it provides the effect in both genders and age. The health impact of public for inaccurate estimates, inconsistent intelligence, conflicting messages, or resulting in misinformation can increase awareness among people and also induce panic situations that accompany major outbreaks of COVID-19.


Subject(s)
COVID-19 , Aged, 80 and over , Female , Humans , Male , Middle Aged , Odds Ratio , Pandemics , SARS-CoV-2 , Survival Analysis
19.
BMC Pulm Med ; 21(1): 338, 2021 Oct 29.
Article in English | MEDLINE | ID: covidwho-1486570

ABSTRACT

Severe coronavirus disease 2019 (COVID-19) accompanies hypercytokinemia, similar to secondary hemophagocytic lymphohistiocytosis (sHLH). We aimed to find if HScore could predict disease severity in COVID-19. HScore was calculated in hospitalized children and adult patients with a proven diagnosis of COVID-19. The need for intensive care unit (ICU), hospital length of stay (LOS), and in-hospital mortality were recorded. The median HScore was 43.0 (IQR 0.0-63.0), which was higher in those who needed ICU care (59.7, 95% CI 46.4-72.7) compared to those admitted to non-ICU medical wards (38.8, 95% CI 32.2-45.4; P = 0.003). It was also significantly higher in patients who died of COVID-19 (105.1, 95% CI 53.7-156.5) than individuals who survived (41.5, 95% CI 35.8-47.1; P = 0.005). Multivariable logistic regression analysis revealed that higher HScore was associated with a higher risk of ICU admission (adjusted OR = 4.93, 95% CI 1.5-16.17, P = 0.008). The risk of death increased by 20% for every ten units increase in HScore (adjusted OR 1.02, 95% CI 1.00-1.04, P = 0.009). Time to discharge was statistically longer in high HScore levels than low levels (HR = 0.41, 95% CI 0.24-0.69). HScore is much lower in patients with severe COVID-19 than sHLH. Higher HScore is associated with more ICU admission, more extended hospitalization, and a higher mortality rate. A modified HScore with a new cut-off seems more practical in predicting disease severity in patients with severe COVID-19.


Subject(s)
COVID-19/diagnosis , Severity of Illness Index , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/pathology , COVID-19/therapy , COVID-19 Testing , Child , Child, Preschool , Critical Care/statistics & numerical data , Cytokine Release Syndrome/diagnosis , Cytokine Release Syndrome/virology , Female , Hospital Mortality , Hospitalization , Humans , Infant , Iran/epidemiology , Length of Stay/statistics & numerical data , Logistic Models , Male , Middle Aged , Predictive Value of Tests , Prognosis , Prospective Studies , Survival Analysis , Young Adult
20.
Eur Rev Med Pharmacol Sci ; 25(19): 5889-5903, 2021 10.
Article in English | MEDLINE | ID: covidwho-1478931

ABSTRACT

OBJECTIVE: Evidence supports a sex disparity in clinical outcomes of COVID-19 patients, with men exhibiting higher mortality rates compared to women. We aimed to test the correlation between serum levels of sex hormones [total testosterone, estradiol (E2), estradiol to testosterone (E2/T) ratio, progesterone), prolactin and 25-hydroxyvitamin D [25(OH)D] and markers of inflammation, coagulation and sepsis at admission in hospitalized men with COVID-19. PATIENTS AND METHODS: We conducted an exploratory retrospective study including symptomatic men with confirmed SARS-CoV-2 infection who were consecutively admitted to our Institution between April 1 and May 31, 2020. RESULTS: Patients were divided into survivors (n=20) and non-survivors (n=39). As compared to survivors, non-survivors showed significantly higher median neutrophil-to-lymphocyte ratio (NLR) values, D-dimer and procalcitonin (PCT) levels, along with significantly lower median 25(OH)D levels and total testosterone levels. Non-survivors exhibited significantly higher median values of E2/T ratio (a marker of aromatase activity). Spearman's correlation analysis revealed that total testosterone levels were significantly and inversely correlated with NLR, high-sensitivity C-reactive protein (hsCRP), interleukin-6, D-dimer and PCT. Conversely, E2/T ratio values were significantly and positively correlated with the aforementioned markers and with white blood cell (WBC) count. In a multivariate analysis performed by a logistic regression model after adjusting for major confounders (age, body mass index, hypertension and cardiovascular disease, diabetes mellitus and malignancy), total testosterone levels were significantly and inversely associated with risk of COVID-19-related in-hospital mortality. CONCLUSIONS: Low total testosterone levels and elevated E2/T ratio values at admission are associated with hyperinflammatory state in hospitalized men with COVID-19. Low total testosterone levels at admission represent an independent risk factor for in-hospital mortality in such patients. Therefore, total testosterone and E2/T ratio may serve as prognostic markers of disease severity in this population.


Subject(s)
COVID-19/blood , COVID-19/mortality , Estradiol/blood , Inflammation/blood , Inflammation/etiology , Testosterone/blood , Vitamin D/analogs & derivatives , Adult , Aged , Aged, 80 and over , Fibrin Fibrinogen Degradation Products/analysis , Hospital Mortality , Hospitalization , Humans , Leukocyte Count , Lymphocyte Count , Male , Middle Aged , Procalcitonin/blood , Retrospective Studies , Risk Factors , Severity of Illness Index , Survival Analysis , Vitamin D/blood
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